Defending Regression Learners Against Poisoning Attacks

• URL: http://arxiv.org/abs/2008.09279v1
• Date: Fri, 21 Aug 2020 03:02:58 GMT
• Title: Defending Regression Learners Against Poisoning Attacks
• Authors: Sandamal Weerasinghe, Sarah M. Erfani, Tansu Alpcan, Christopher Leckie, Justin Kopacz
• Abstract summary: We introduce a novel Local Intrinsic Dimensionality (LID) based measure called N-LID that measures the local deviation of a given data point's LID with respect to its neighbors. N-LID can distinguish poisoned samples from normal samples and propose an N-LID based defense approach that makes no assumptions of the attacker. We show that the proposed defense mechanism outperforms the state of the art defenses in terms of prediction accuracy (up to 76% lower MSE compared to an undefended ridge model) and running time.
• Score: 25.06658793731661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Regression models, which are widely used from engineering applications to financial forecasting, are vulnerable to targeted malicious attacks such as training data poisoning, through which adversaries can manipulate their predictions. Previous works that attempt to address this problem rely on assumptions about the nature of the attack/attacker or overestimate the knowledge of the learner, making them impractical. We introduce a novel Local Intrinsic Dimensionality (LID) based measure called N-LID that measures the local deviation of a given data point's LID with respect to its neighbors. We then show that N-LID can distinguish poisoned samples from normal samples and propose an N-LID based defense approach that makes no assumptions of the attacker. Through extensive numerical experiments with benchmark datasets, we show that the proposed defense mechanism outperforms the state of the art defenses in terms of prediction accuracy (up to 76% lower MSE compared to an undefended ridge model) and running time.

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